Does roof shape matter? Solar photovoltaic (PV) integration on building roofs

This paper focuses on roof-shape classification and solar potential for integrating photovoltaics (PV) on roofs. A machine-learning approach, Support Vector Machine (SVM), is used to classify the roof shapes in the city of Geneva. The impact of various roof characteristics on the solar potential is assessed and analyzed. Monthly solar irradiation on rooftops, freely accessible from www.ge.ch/sitg using GIS LiDAR data (resolution 50cm by 50cm), is used in the analysis. The SVM identifies 6 types of roof shapes correctly in 66% of cases, that is, flat, gable, hip, gambrel & mansard, cross/corner gable & hip, and complex roof. We rank the roofs based on the complexity of their shape, useful area for PV, and potential for receiving solar energy. The results show that for most roof shapes the ratio between useful roof areas and building footprint area is close to one, the main exception being gable where the ratio is 1.18, suggesting that footprint is a good measure of useful PV roof area. The flat roof has the second highest useful roof area for PV (complex roof being the highest) and the highest PV potential (in GWh). By contrast, hip roof has the lowest PV potential. Solar roof-shape classification provides basic information for designing new buildings, retrofitting interventions on the building roofs and efficient solar integration on rooftops. The results for the city of Geneva suggest that data-driven approach is very useful for roof-shape classification which can be expanded to the national scale.

A city-scale roof shape classification using machine learning for solar energy applications

Dan Assouline, Andreas Bill, Bérénice Guiboud, Nahid Mohajeri Pour Rayeni, Jean-Louis Scartezzini

Solar energy deployment through PV installations in urban areas depends strongly on the shape, size, and orientation of available roofs. Here we use a machine learning approach, Support Vector Machine (SVM) classification, to classify 10,085 building roofs in relation to their received solar energy in the city of Geneva in Switzerland. The SVM correctly identifies six types of roof shapes in 66% of cases, that is, flat & shed, gable, hip, gambrel & mansard, cross/corner gable & hip, and complex roofs.We classify the roofs based on their useful area for PV installations and potential for receiving solar energy. For most roof shapes, the ratio between useful roof area and building footprint area is close to one, suggesting that footprint is a good measure of useful PV roof area. The main exception is the gable where this ratio is 1.18. The flat and shed roofs have the second highest useful roof area for PV (complex roof being the highest) and the highest PV potential (in GWh). By contrast, hip roof has the lowest PV potential. Solar roof-shape classification provides basic information for designing new buildings, retrofitting interventions on the building roofs, and efficient solar integration on the roofs of buildings.

2018

Optimisation of Urban Form by the Evaluation of the Solar Potential

Marylène Montavon

What will the cities of tomorrow look like? This is a crucial question because in the four and a half billion years of its existence, the Earth has never been so threatened, as it is today. Since 2008 half of the world's population has lived in towns – more than 70% in rich nations. Three quarters of CO2 emissions are produced in towns, 75% of the world's energy is consumed in towns. The battle for our climate, for the continuation of our species and all other life, will be largely waged in cities. Will it be possible to create the ideal ecological town? The objective of this study is to compare actual and theoretical urban forms in order to explore the diverse effects of daylighting and solar potential on densely built-up sites. Indeed, the search for ideal solar orientations has been a persistent problem from the 19th century until the beginning of the 20th. Even today, for all our scientific knowledge and empirical experience of solar energy, making the correct choice has still not crossed the boundary from an enigmatic, tricky art into the field of an exact science. As a first step, six existing urban sites in Switzerland and Brazil, as well as two projects in the United Kingdom, a few generic models and a utopian city, were modelled and analyzed. The goal was not merely the construction of new, exemplary districts but also the transformation of existing towns. The findings have been used to assess the potential of façades and roofs located in urban areas for active and passive solar heating, photovoltaic electricity generation and daylighting. The results obtained from these different case studies revealed large variations of the potential for solar energy collection on the buildings' façades and roofs. Ideally, these investigations are expected to help architects and town planners understand how to optimise solar collection on buildings design. In addition, a study was made of the Contemporary City of Three Million Inhabitants created by Le Corbusier. According to the latter, the Contemporary City could increase the urban capacity and at the same time improve the urban environment and the efficiency of the city. Therefore the present study extracted the solar potential of this utopian city in order to assess Le Corbusier's propositions. Since 1922, Le Corbusier's studies have influenced architects and town-planners in their choice of orientations and urban forms: it was therefore crucial to discover if this reliance on Le Corbusier was justified. Solar irradiation and illuminance values obtained through numerical simulations form the core part of the method. The method adopted for the entire set of computer simulations consisted in modelling — via a computer-aided drawing piece of software, namely AutoCAD — the digital model of each building or group of buildings prior to exporting them to an urban performance analysis software (PPF/RADIANCE software package). Spatial distributions of solar irradiation and daylight fluxes over the overall building façades and roofs were calculated using ray-tracing simulation techniques to determine the appropriate placement of different solar technologies (passive and active solar, photovoltaic and daylighting). Performance indicators were used to assess the solar utilisation potential of the urban sites (e.g. statistics of sky view factors and daylight factors) in order to determine the optimal solar strategies for a given urban context. In addition, various works and sun-related recommendations that have appeared mainly since the end of the nineteenth century, together with the various scientific models and software tools pertaining to this topic, are presented in this study.

EPFL2010

A machine learning methodology for estimating roof-top photovoltaic solar energy potential in Switzerland

Dan Assouline, Nahid Mohajeri Pour Rayeni, Jean-Louis Scartezzini

Solar photovoltaic (PV) deployment on existing building roof-tops has proven to be one of the most viable large scale resources of sustainable energy for urban areas. While there have been many studies on roof-top integrated PV systems at building and neighborhood scale, estimating the PV energy potential through the available roof surface area in large scale, however, remains a challenge. This study proposes a methodology to estimate the roof-top solar PV potential for existing buildings at the commune level (the smallest administrative division) in Switzerland. In addition, while several studies suggest physical models to assess the photovoltaic solar energy potential, the current study proposes a computational data-based learning approach, using the following four steps: (1) monthly estimation of the main solar irradiance components (global horizontal, direct normal, diffuse horizontal) for the entire Switzerland, that is, the total amount of solar energy available from the Sun (2) processing of training and testing population and building data at a commune level, (3) estimation of the available roof-top surface (AR), average roof tilted angle (), and average shading coeffcient (S) for buildings in the urban areas, for each commune, (4) combination of the available roof-top surface area for each commune with the solar potential along with other parameters so as to estimate the actual solar PV potential. The first step is achieved through a Support Vector Regression (SVR), using solar irradiance satellite data with an average RMSE of 1.68 W/m2. In the second step, GIS have been used to aggregate the data at a commune level. In the third step, a supervised learning algorithm using SVR has been used so as to estimate AR, β, and S in existing buildings, which can be extremely difficult data to obtain at a large scale. We use (i) population and building density data, (ii) land use data, and (iii) building typologies as input features. Aggregated values for AR, β and S for 46 communes in Geneva canton are used as a training output (labeled data) for the learning process. Finally, in the fourth step, by combining solar irradiance and building parameters predictions, we estimate the solar PV potential at the national scale.